The present disclosure generally relates to systems and methods for monitoring and/or controlling states of a subject, and more specifically to systems and methods for monitoring and/or controlling states of a subject using a model-based characterization of the dynamic time-frequency structure associated with physiological data.
In the case of surface recordings of brain activity, it was demonstrated over 75 years ago that central nervous system changes, such as those occurring during sleep or as a result of administration of an anesthetic, are observable via neural EEG recordings, which measure electrical impulses in the brain through electrodes placed on the scalp. As a consequence, it was postulated that EEG information could be used to track in real time the brain states of patients, for instance during sleep, or under sedation and general anesthesia, the same way that an electrocardiogram could be used to track the state of the heart and the cardiovascular system.
Tools used by clinicians for monitoring brain states of patients include physiologically and EEG-based systems developed to help measure neural network activity resulting from certain biological processes, task activity, sleep, anesthetic administration, and other clinical procedures. For example, such monitoring systems are used to track the level of consciousness of a patient undergoing general anesthesia or sedation in the operating room and intensive care unit. Using proprietary algorithms that combine spectral and entropy information derived from EEG data, such systems provide feedback through partial or amalgamized representations of the acquired signals for use in identifying the brain state of a patient. In some scenarios, direct manipulation of the central nervous system, often performed using pharmacological approaches, is facilitated using such systems by way of controlling the level unconsciousness, amnesia, analgesia, and immobility of a patient. For example, during sleep, EEG, EOG, EMG, and respiration data is monitored in clinical or home settings, and then evaluated through visual analysis to diagnose sleep and respiratory disorders.
In order to examine specific spectral signatures of underlying neural activity, it has been an emerging practice to compute time-frequency representations of the acquired EEG data, using techniques including but not limited to spectrograms (FFT, Hanning window), multitaper spectrograms, wavelet transforms, Gabor transforms, and chirplet transforms. Different approaches previously proposed characterized measured neural rhythms at several discrete time periods, using methods that describe time-varying spectral signatures qualitatively. For instance, one attempt for tracking time-frequency features included modeling spectral content using pure sinusoids that have non-stationary peaks and amplitudes. Specifically, the sum of more than one sinusoid model was used to track multiple simultaneously-evolving oscillations. While this previous approach may be adequate for tracking pure sinusoids produced by artificial or mechanical systems, such pure sinusoids are almost never present in physiological systems.
For example, measured EEG signals often exhibit broadband peaks in the time-frequency domain, the full structure of which can provide important information about the underlying neural activity. In particular, the specific form of a spectral peak, describing EEG data acquired during, say, administration of propofol during general anesthesia, can provide information about a patient's depth-of-consciousness. Hence, current methods that employ sinusoidal models of time-frequency structure are lacking since such methods collapse broadband physiological oscillations to a single frequency, thereby ignoring information content present in the peak bandwidth and structure. It is therefore necessary to devise a system in which the full spectral structure and the information contained there in are retained.
Considering the above, there continues to be a need for systems and methods to quantitatively and accurately analyze physiological data dynamics for monitoring patients and based thereon, provide systems and methods for controlling patient states, such as during sedation, general anesthesia, sleep, medically induced coma, hypothermia, drug delivery, or other natural or pharmacologically-mediated dynamic neural scenarios.